• Create a web page presentation using R Markdown that features a plot created with Plotly.
• Host your webpage on either GitHub Pages, RPubs, or NeoCities.
• The webpage must contain the date that you created the document, and it must contain a plot created with Plotly.

Chosen Data Source

We will be exploring the wine quality data set from the UCI Machine Learning Repository http://archive.ics.uci.edu/ml/datasets/Wine+Quality Two datasets are included. They are related to red and white variants of the Portuguese “Vinho Verde” wine.

References P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

Input variables (based on physicochemical tests):

• fixed acidity, volatile acidity, citric acid
• residual sugar
• chlorides
• free sulfur dioxide, total sulfur dioxide
• density
• pH
• sulphates
• alcohol

Output variable (based on sensory data):

• quality (score between 0 and 10)

Lets load the Red wine data

red_wine <- read.csv(file = './Data/winequality-red.csv', header = TRUE, sep = ';')

We will explore a small subset of attributes and the impact on quality of the wine.

q1 <- ggplotly(ggplot(data = red_wine, 
                      aes(x = sulphates, y = pH, color = quality)) +
                 geom_point() +
  scale_color_gradientn(colours = rainbow(5)) + 
  labs( x = "Sulphates", y = "pH", color = "Quality Rating")) 
q2 <- ggplotly(ggplot(data = red_wine, 
                      aes(x = residual.sugar, y = citric.acid, color = quality)) + 
                 geom_point() +
  scale_color_gradientn(colours = rainbow(5)) + 
  labs( x = "Residual Sugar", y = "Cirtic Acid", color = "Quality Rating"))
q3 <- ggplotly(ggplot(data = red_wine, 
                      aes(x = chlorides, y = total.sulfur.dioxide,
                          color = quality)) +
                 geom_point() +
  scale_color_gradientn(colours = rainbow(5)) + 
  labs( x = "Chlorides", y = "Total Sulfur Dioxide", color = "Quality Rating"))
q4 <- ggplotly(ggplot(data = red_wine, 
                      aes(x = alcohol, y = density, color = quality)) + 
                 geom_point() +
  scale_color_gradientn(colours = rainbow(5))+ 
  labs( x = "Alcohol content", y = "Density", color = "Quality Rating"))

subplot(q1,q2,q3,q4, titleX = TRUE, titleY = TRUE, nrows = 2, margin = 0.09)